Response Time Model Research Articles

Modelling Conditional Dependence Between Response Time and Accuracy.

The assumption of conditional independence between response time and accuracy given speed and ability is commonly made in response time modelling. However, this assumption might be violated in some cases, meaning that the relationship between the response time and the response accuracy of the same item cannot be fully explained by the correlation between the overall speed and ability. We propose to explicitly model the residual dependence between time and accuracy by incorporating the effects of the residual response time on the intercept and the slope parameter of the IRT model for response accuracy. We present an empirical example of a violation of conditional independence from a low-stakes educational test and show that our new model reveals interesting phenomena about the dependence of the item properties on whether the response is relatively fast or slow. For more difficult items responding slowly is associated with a higher probability of a correct response, whereas for the easier items responding slower is associated with a lower probability of a correct response. Moreover, for many of the items slower responses were less informative for the ability because their discrimination parameters decrease with residual response time.

Quantitative Chemical Measurements of Vesicular Transmitters with Electrochemical Cytometry.

Electrochemical cytometry adds a new dimension to our ability to study the chemistry and chemical storage of transmitter molecules stored in nanometer vesicles. The approach involves the adsorption and subsequent rupture of vesicles on an electrode surface during which the electroactive contents are quantitatively oxidized (or reduced). The measured current allows us to count the number of molecules in the vesicles using Faraday's law and to correlate this to the amount of molecules released when single exocytosis events take place at communicating cells. The original format for this method involved a capillary electrophoresis separation step to singly address each vesicle, but we have more recently discovered that cellular vesicles tend to adsorb to carbon electrodes and spontaneously as well as stochastically rupture to give mostly single vesicle events. This approach, called impact electrochemical cytometry, even though the impact is perhaps not the important part of this process, has been studied and the vesicle rupture appears to be at the interface between the vesicle and the electrode and is probably driven by electroporation. The pore size and rate of content electrolysis are a function of the pore diameter and the presence of a protein core in the vesicles. In model liposomes with no protein, events appear extremely rapidly as the soft nanoparticles impact the electrode and the contents are oxidized. It appears that the proteins decorating the surface of the vesicle are important in maintaining a gap from the electrode and when this gap is closed electroporation takes place. Models of the event response times suggest the pores formed are small enough so we can carry out these measurements at nanotip electrodes and we have used this to quantify the vesicle content in living cells in a mode we call intracellular impact electrochemical cytometry. The development of electrochemical cytometry allows comparison between vesicle content and vesicular release and we have found that only part of the vesicle content is released in typical exocytotic cases measured by amperometry. This has led to the novel hypothesis that most exocytosis from dense core vesicles is via mechanism where vesicles fuse with the cell membrane, some content is released and then close again to be reloaded and reused. It leaves open the possibility that cells regulate release during individual events. This might be important in learning and memory and be a nonreceptor pharmaceutical target for brain-related disorders. Indeed, the concept of the chemo-brain observed in cisplatin-treated cancer patients appears to be at least in part the result of changing the fraction of transmitter released and we have been able to show this by using the combined amperometric measurement of release and electrochemical cytometry at model cells.

Response moderation models for conditional dependence between response time and response accuracy.

It is becoming more feasible and common to register response times in the application of psychometric tests. Researchers thus have the opportunity to jointly model response accuracy and response time, which provides users with more relevant information. The most common choice is to use the hierarchical model (van der Linden, 2007, Psychometrika, 72, 287), which assumes conditional independence between response time and accuracy, given a person's speed and ability. However, this assumption may be violated in practice if, for example, persons vary their speed or differ in their response strategies, leading to conditional dependence between response time and accuracy and confounding measurement. We propose six nested hierarchical models for response time and accuracy that allow for conditional dependence, and discuss their relationship to existing models. Unlike existing approaches, the proposed hierarchical models allow for various forms of conditional dependence in the model and allow the effect of continuous residual response time on response accuracy to be item-specific, person-specific, or both. Estimation procedures for the models are proposed, as well as two information criteria that can be used for model selection. Parameter recovery and usefulness of the information criteria are investigated using simulation, indicating that the procedure works well and is likely to select the appropriate model. Two empirical applications are discussed to illustrate the different types of conditional dependence that may occur in practice and how these can be captured using the proposed hierarchical models.

The shifted Wald distribution for response time data analysis.

We propose and demonstrate the shifted Wald (SW) distribution as both a useful measurement tool and intraindividual process model for psychological response time (RT) data. Furthermore, we develop a methodology and fitting approach that readers can easily access. As a measurement tool, the SW provides a detailed quantification of the RT data that is more sophisticated than mean and SD comparisons. As an intraindividual process model, the SW provides a cognitive model for the response process in terms of signal accumulation and the threshold needed to respond. The details and importance of both of these features are developed, and we show how the approach can be easily generalized to a variety of experimental domains. The versatility and usefulness of the approach is demonstrated on 3 published data sets, each with a different canonical mode of responding: manual, vocal, and oculomotor modes. In addition, model-fitting code is included with the article. (PsycINFO Database Record

A Poisson Random Walk Model for Response Time and Pure Accuracy Tasks

A Poisson Random Walk Model for Response Time and Pure Accuracy Tasks

Functional mechanisms of probabilistic inference in feature- and space-based attentional systems

Humans flexibly attend to features or locations and these processes are influenced by the probability of sensory events. We combined computational modeling of response times with fMRI to compare the functional correlates of (re-)orienting, and the modulation by probabilistic inference in spatial and feature-based attention systems. Twenty-four volunteers performed two task versions with spatial or color cues. Percentage of cue validity changed unpredictably. A hierarchical Bayesian model was used to derive trial-wise estimates of probability-dependent attention, entering the fMRI analysis as parametric regressors. Attentional orienting activated a dorsal frontoparietal network in both tasks, without significant parametric modulation. Spatially invalid trials activated a bilateral frontoparietal network and the precuneus, while invalid feature trials activated the left intraparietal sulcus (IPS). Probability-dependent attention modulated activity in the precuneus, left posterior IPS, middle occipital gyrus, and right temporoparietal junction for spatial attention, and in the left anterior IPS for feature-based and spatial attention. These findings provide novel insights into the generality and specificity of the functional basis of attentional control. They suggest that probabilistic inference can distinctively affect each attentional subsystem, but that there is an overlap in the left IPS, which responds to both spatial and feature-based expectancy violations.

Guest Editorial

Guest Editorial

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We first demonstrate a novel high-speed and highpower p-i-n photodiode with a cutoff wavelength at ~2.5 μm. This device is composed of a partially depleted p-type Ga <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.8</sub> In <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.2</sub> As <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.16</sub> Sb <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.84</sub> photo-absorption layer grown on GaSb substrate in order to enhance its speed and saturation power performance. With proper passivation processes, a low dark current (~0.7 μA) and wide (6 GHz) optical-to-electrical (O-E) bandwidth can be simultaneously achieved with a miniaturized size of active mesa diameter (~8 μm) under 1.55-μm optical wavelength excitation. The electron/hole drift-velocity and electron mobility across the Ga <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.8</sub> In <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.2</sub> As <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.16</sub> Sb <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.84</sub> active layer can further be estimated based on the extracted internal response time and device modeling technique. Using the heterodyne-beating setup at 1.55-μm wavelength, the demonstrated device shows a saturation current at ~3.6 mA (at 6-GHz operating frequency), which is mainly limited by the space-charge (electron/hole) induced screening effect at the depleted absorption region.

The EZ diffusion model provides a powerful test of simple empirical effects

Over the last four decades, sequential accumulation models for choice response times have spread through cognitive psychology like wildfire. The most popular style of accumulator model is the diffusion model (Ratcliff Psychological Review, 85, 59–108, 1978), which has been shown to account for data from a wide range of paradigms, including perceptual discrimination, letter identification, lexical decision, recognition memory, and signal detection. Since its original inception, the model has become increasingly complex in order to account for subtle, but reliable, data patterns. The additional complexity of the diffusion model renders it a tool that is only for experts. In response, Wagenmakers et al. (Psychonomic Bulletin & Review, 14, 3–22, 2007) proposed that researchers could use a more basic version of the diffusion model, the EZ diffusion. Here, we simulate experimental effects on data generated from the full diffusion model and compare the power of the full diffusion model and EZ diffusion to detect those effects. We show that the EZ diffusion model, by virtue of its relative simplicity, will be sometimes better able to detect experimental effects than the data–generating full diffusion model.

Item Response Theory

This review introduces classical item response theory (IRT) models as well as more contemporary extensions to the case of multilevel, multidimensional, and mixtures of discrete and continuous latent variables through the lens of discrete multivariate analysis. A general modeling framework is discussed, and the applications of this framework in diverse contexts are presented, including large-scale educational surveys, randomized efficacy studies, and diagnostic measurement. Other topics covered include parameter estimation and model fit evaluation. Both classical (numerical integration based) and more modern (stochastic) parameter estimation approaches are discussed. Similarly, limited information goodness-of-fit testing and posterior predictive model checking are reviewed and contrasted. The review concludes with a discussion of some emerging strands in IRT research such as response time modeling, crossed random effects models, and non-standard models for response processes.

Grade of Membership Response Time Model for Detecting Guessing Behaviors

A response model that is able to detect guessing behaviors and produce unbiased estimates in low-stake conditions using timing information is proposed. The model is a special case of the grade of membership model in which responses are modeled as partial members of a class that is affected by motivation and a class that responds only according to the level of ability. Monte Carlo simulations were conducted to compare the proposed model with an approach that ignored guessing and an approach based on item filtering. In each simulated condition, the proposed model outperformed the other approaches by showing the lowest level of bias and the highest precision of item and persons estimates. Finally, the model was estimated using real life data from Programme for the International Assessment of Adult Competencies research (PIAAC). The results showed slight but expected corrections for the levels of proficiency in all countries.

The effect of alcohol hangover on choice response time.

The effect of alcohol hangover on cognitive processing has received little attention. We explored the effect of alcohol hangover on choice response time (RT), a dominant dependent variable (DV) in cognitive research. Prior research of the effect of hangover on RT has produced mixed findings; all studies reviewed relied exclusively on estimates of central tendency (e.g. mean RT), which has limited information value. Here we present novel analytical methods by going beyond mean RT analysis. Specifically, we examined performance in hangover conditions (n=31) across the whole RT distribution by fitting ex-Gaussian models to participant data, providing a formal description of the RT distribution. This analysis showed detriments to performance under hangover conditions at the slower end of the RT distribution and increased RT variance under hangover conditions. We also fitted an explicit mathematical process model of choice RT - the diffusion model - which estimates parameters reflecting psychologically-meaningful processes underlying choice RT. This analysis showed that hangover reduced information processing efficiency during response selection, and increased response caution; changes in these parameters reflect hangover affecting core decisional-components of RT performance. The implications of the data as well as the methods used for hangover research are discussed.

Evaluating the consequences of impaired monitoring of learned behavior in attention-deficit/hyperactivity disorder using a Bayesian hierarchical model of choice response time.

Performance monitoring deficits have been proposed as a cognitive marker involved in the development of attention-deficit/hyperactivity disorder (ADHD), but it is unclear whether these deficits cause impairment when established action sequences conflict with environmental demands. The current study applies a novel data-analytic technique to a well-established sequence learning paradigm to investigate reactions to disruption of learned behavior in ADHD. Children (ages 8-12) with and without ADHD completed a serial reaction time task in which they implicitly learned an 8-item sequence of keypresses over 5 training blocks. The training sequence was replaced with a novel sequence in a transfer block, and returned in 2 subsequent recovery blocks. Response time (RT) data were fit by a Bayesian hierarchical version of the linear ballistic accumulator model, which permitted the dissociation of learning processes from performance monitoring effects on RT. Sequence-specific learning on the task was reflected in the systematic reduction of the amount of evidence required to initiate a response, and was unimpaired in ADHD. When the novel sequence onset, typically developing children displayed a shift in their attentional state while children with ADHD did not, leading to worse subsequent performance compared to controls. Children with ADHD are not impaired in learning novel action sequences, but display difficulty monitoring their implementation and engaging top-down control when they become inadequate. These results support theories of ADHD that highlight the interactions between monitoring processes and changing cognitive demands as the cause of self-regulation and information-processing problems in the disorder. (PsycINFO Database Record

Semiparametric Bayesian approaches to systems factorial technology

Systems factorial technology (e.g., Townsend and Nozawa, 1995) is a toolbox of methods to test characteristics of multichannel processing systems. In this paper, we present a semiparametric Bayesian model of response times based on a piecewise-exponential model. This model allows us to compute the posterior estimates of the response time distribution hazard function and the systems factorial technology functionals that can be computed from it, including the survivor function interaction contrast and the capacity coefficient. We then compute posterior estimates of statistics that reflect different behaviors of the functionals, and so arrive at a fully Bayesian and theoretically-motivated approach to systems factorial technology. Simulation studies demonstrate the effectiveness of the approach, and we apply it to a published data set.

A simplified version of the maximum information per time unit method in computerized adaptive testing.

In this article, we propose a simplified version of the maximum information per time unit method (MIT; Fan, Wang, Chang, & Douglas, Journal of Educational and Behavioral Statistics 37: 655-670, 2012), or MIT-S, for computerized adaptive testing. Unlike the original MIT method, the proposed MIT-S method does not require fitting a response time model to the individual-level response time data. It is also computationally efficient. The performance of the MIT-S method was compared against that of the maximum information (MI) method in terms of measurement precision, testing time saving, and item pool usage under various item response theory (IRT) models. The results indicated that when the underlying IRT model is the two- or three-parameter logistic model, the MIT-S method maintains measurement precision and saves testing time. It performs similarly to the MI method in exposure control; both result in highly skewed item exposure distributions, due to heavy reliance on the highly discriminating items. If the underlying model is the one-parameter logistic (1PL) model, the MIT-S method maintains the measurement precision and saves a considerable amount of testing time. However, its heavy reliance on time-saving items leads to a highly skewed item exposure distribution. This weakness can be ameliorated by using randomesque exposure control, which successfully balances the item pool usage. Overall, the MIT-S method with randomesque exposure control is recommended for achieving better testing efficiency while maintaining measurement precision and balanced item pool usage when the underlying IRT model is 1PL.

Limited information estimation of the diffusion-based item response theory model for responses and response times.

Psychological tests are usually analysed with item response models. Recently, some alternative measurement models have been proposed that were derived from cognitive process models developed in experimental psychology. These models consider the responses but also the response times of the test takers. Two such models are the Q-diffusion model and the D-diffusion model. Both models can be calibrated with the diffIRT package of the R statistical environment via marginal maximum likelihood (MML) estimation. In this manuscript, an alternative approach to model calibration is proposed. The approach is based on weighted least squares estimation and parallels the standard estimation approach in structural equationmodelling. Estimates are determined by minimizing the discrepancy between the observed and the implied covariance matrix. The estimator is simple to implement, consistent, and asymptotically normally distributed. Least squares estimation also provides a test of model fit by comparing the observed and implied covariance matrix. The estimator and the test of model fit are evaluated in a simulation study. Although parameter recovery is good, the estimator is less efficient than the MML estimator.

Fusiform Gyrus Dysfunction is Associated with Perceptual Processing Efficiency to Emotional Faces in Adolescent Depression: A Model-Based Approach.

While the extant literature has focused on major depressive disorder (MDD) as being characterized by abnormalities in processing affective stimuli (e.g., facial expressions), little is known regarding which specific aspects of cognition influence the evaluation of affective stimuli, and what are the underlying neural correlates. To investigate these issues, we assessed 26 adolescents diagnosed with MDD and 37 well-matched healthy controls (HCL) who completed an emotion identification task of dynamically morphing faces during functional magnetic resonance imaging (fMRI). We analyzed the behavioral data using a sequential sampling model of response time (RT) commonly used to elucidate aspects of cognition in binary perceptual decision making tasks: the Linear Ballistic Accumulator (LBA) model. Using a hierarchical Bayesian estimation method, we obtained group-level and individual-level estimates of LBA parameters on the facial emotion identification task. While the MDD and HCL groups did not differ in mean RT, accuracy, or group-level estimates of perceptual processing efficiency (i.e., drift rate parameter of the LBA), the MDD group showed significantly reduced responses in left fusiform gyrus compared to the HCL group during the facial emotion identification task. Furthermore, within the MDD group, fMRI signal in the left fusiform gyrus during affective face processing was significantly associated with greater individual-level estimates of perceptual processing efficiency. Our results therefore suggest that affective processing biases in adolescents with MDD are characterized by greater perceptual processing efficiency of affective visual information in sensory brain regions responsible for the early processing of visual information. The theoretical, methodological, and clinical implications of our results are discussed.

Expected Response Time Model Considering Churn Rate for Dynamic IoT Devices

Background/Objectives: IoT has dynamic characteristics that the devices are freely joined or leave the networks. In this paper, we propose an expected response time model in a dynamic IoT environment. Methods/Statistical Analysis: We analyze the expected response time of an IoT query for star, tree, and mesh topologies. We evaluate the proposed model by comparing how it affects to response time according to the network topology and the bounce rate. Findings: We found that the impact of churn rates shows significantly different results under various topologies. Thus, churn rate is an important consideration in dynamic device replacement procedure that replaces a faulty device with a relatively healthy device. Improvements: Our analytical tool can be used for a decision mechanism of the IoT node that will operate most effective for time-critical IoT applications.

Supplemental Material for Evaluating the Consequences of Impaired Monitoring of Learned Behavior in Attention-Deficit/Hyperactivity Disorder Using a Bayesian Hierarchical Model of Choice Response Time

Supplemental Material for Evaluating the Consequences of Impaired Monitoring of Learned Behavior in Attention-Deficit/Hyperactivity Disorder Using a Bayesian Hierarchical Model of Choice Response Time

Response-time evidence for mixed memory states in a sequential-presentation change-detection task

Response-time (RT) and choice-probability data were obtained in a rapid visual sequential-presentation change-detection task in which memory set size, study-test lag, and objective change probabilities were manipulated. False “change” judgments increased dramatically with increasing lag, consistent with the idea that study items with long lags were ejected from a discrete-slots buffer. Error RTs were nearly invariant with set size and lag, consistent with the idea that the errors were produced by a stimulus-independent guessing process. The patterns of error and RT data could not be explained in terms of encoding limitations, but were consistent with the hypothesis that long retention lags produced a zero-stimulus-information state that required guessing. Formal modeling of the change-detection RT and error data pointed toward a hybrid model of visual working memory. The hybrid model assumed mixed states involving a combination of memory and guessing, but with higher memory resolution for items with shorter retention lags. The work raises new questions concerning the nature of the memory representations that are produced across the closely related tasks of change detection and visual memory search.